AI Research Scientist :

We are seeking a highly skilled and innovative Research Scientist specializing in Artificial Intelligence to join our R&D team. This role is ideal for someone with deep expertise in implementing and customizing state-of-the-art AI research papers, optimizing large-scale models, and building robust datasets. You will work on real-world applications of generative AI, large language models (LLMs), and computer vision systems, contributing directly to the development of scalable and impactful AI solutions.

Key Responsibilities :

- Implement and customize cutting-edge AI research papers into working prototypes and production-ready models.

- Conduct performance improvement experiments on large-scale models, including fine-tuning, prompt engineering, and architecture modifications.

- Develop and manage training and evaluation datasets, including synthetic data generation, annotation pipelines, and benchmark creation.

- Collaborate with engineering and product teams to deploy research outputs into scalable systems.

- Stay up-to-date with the latest advancements in AI and contribute to internal research strategy.

- Document experiments, share insights, and support innovation through patent filings and technical reports.

Required Qualifications :

- Ph.D. or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

- Proven experience in implementing and customizing AI research papers (e.g., from arXiv, NeurIPS, ICML).

Tech Stack :

- Large Language Models (LLMs) : GPT, LLaMA, Mistral, Falcon.

- Vision Models : ViT, SAM, CLIP.

- Generative Models : Stable Diffusion, DALL-E, GANs.

- Multimodal Systems combining vision, language, and audio.

- Python, PyTorch, TensorFlow, JAX.

- Hugging Face Transformers, Diffusers, OpenAI Gym.

- Weights & Biases, MLflow, TensorBoard.

- Distributed training : DeepSpeed, FSDP, Horovod.

- Model optimization : quantization, pruning, distillation.

- Data tools : DVC, Apache Arrow, Label Studio.

- Strong understanding of evaluation metrics, benchmarking, and reproducibility.